Modeling Long-term Dependencies. Sst [1]} and SS-TAD [2]} which are RNN-based methods achieve relatively lower results as they can not generate flexible proposals. PGCN [3]}, G-TAD [4]}, BC-GNN [5]}, AGCN [6]}, ATAG [7]}, and VSGN [8]} are graph models that capture dependencies between proposals or video
segments. Among them VSGN [8]} achieved the best performance by exploiting correlations between cross-scale snippets (original and magnified) and aggregating their features with a graph pyramid network. AGT [10]}, RTD-Net [11]}, ATAG [7]}, and TadTR [13]} use transformers to model long-range dependencies. Among them RTD-Net [11]} achieved the best results (on THUMOS14) by customizing the encoder with a boundary-attentive architecture to enhance the discrimination capability of action boundary.
